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Kumar S, Bhushan B, Kumar A, Panigrahi M, Bharati J, Kumari S, Kaiho K, Banik S, Karthikeyan A, Chaudhary R, Gaur GK, Dutt T. Elucidation of novel SNPs affecting immune response to classical swine fever vaccination in pigs using immunogenomics approach. Vet Res Commun 2024; 48:941-953. [PMID: 38017322 DOI: 10.1007/s11259-023-10262-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/19/2023] [Indexed: 11/30/2023]
Abstract
The host genetic makeup plays a significant role in causing the within-breed variation among individuals after vaccination. The present study was undertaken to elucidate the genetic basis of differential immune response between high and low responder Landlly (Landrace X Ghurrah) piglets vis-à-vis CSF vaccination. For the purpose, E2 antibody response against CSF vaccination was estimated in sampled animals on the day of vaccination and 21-day post-vaccination as a measure of humoral immune response. Double-digestion restriction associated DNA (ddRAD) sequencing was undertaken on 96 randomly chosen Landlly piglets using Illumina HiSeq platform. SNP markers were called using standard methodology. Genome-wide association study (GWAS) was undertaken in PLINK program to identify the informative SNP markers significantly associated with differential immune response. The results revealed significant SNPs associated with E2 antibody response against CSF vaccination. The genome-wide informative SNPs for the humoral immune response against CSF vaccination were located on SSC10, SSC17, SSC9, SSC2, SSC3 and SSC6. The overlapping and flanking genes (500Kb upstream and downstream) of significant SNPs were CYB5R1, PCMTD2, WT1, IL9R, CD101, TMEM64, TLR6, PIGG, ADIPOR1, PRSS37, EIF3M, and DNAJC24. Functional enrichment and annotation analysis were undertaken for these genes in order to gain maximum insights into the association of these genes with immune system functionality in pigs. The genetic makeup was associated with differential immune response against CSF vaccination in Landlly piglets while the identified informative SNPs may be used as suitable markers for determining variation in host immune response against CSF vaccination in pigs.
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Affiliation(s)
- Satish Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India.
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Amit Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India.
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Jaya Bharati
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - Soni Kumari
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Kaisa Kaiho
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Santanu Banik
- ICAR-National Research Centre on Pig, Rani, Guwahati, Assam, 781131, India
| | - A Karthikeyan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Rajni Chaudhary
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - G K Gaur
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
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Zhang J, Liang X, Gonzales S, Liu J, Gao XR, Wang X. A gene based combination test using GWAS summary data. BMC Bioinformatics 2023; 24:2. [PMID: 36597047 PMCID: PMC9811798 DOI: 10.1186/s12859-022-05114-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 12/13/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Gene-based association tests provide a useful alternative and complement to the usual single marker association tests, especially in genome-wide association studies (GWAS). The way of weighting for variants in a gene plays an important role in boosting the power of a gene-based association test. Appropriate weights can boost statistical power, especially when detecting genetic variants with weak effects on a trait. One major limitation of existing gene-based association tests lies in using weights that are predetermined biologically or empirically. This limitation often attenuates the power of a test. On another hand, effect sizes or directions of causal genetic variants in real data are usually unknown, driving a need for a flexible yet robust methodology of gene based association tests. Furthermore, access to individual-level data is often limited, while thousands of GWAS summary data are publicly and freely available. RESULTS To resolve these limitations, we propose a combination test named as OWC which is based on summary statistics from GWAS data. Several traditional methods including burden test, weighted sum of squared score test [SSU], weighted sum statistic [WSS], SNP-set Kernel Association Test [SKAT], and the score test are special cases of OWC. To evaluate the performance of OWC, we perform extensive simulation studies. Results of simulation studies demonstrate that OWC outperforms several existing popular methods. We further show that OWC outperforms comparison methods in real-world data analyses using schizophrenia GWAS summary data and a fasting glucose GWAS meta-analysis data. The proposed method is implemented in an R package available at https://github.com/Xuexia-Wang/OWC-R-package CONCLUSIONS: We propose a novel gene-based association test that incorporates four different weighting schemes (two constant weights and two weights proportional to normal statistic Z) and includes several popular methods as its special cases. Results of the simulation studies and real data analyses illustrate that the proposed test, OWC, outperforms comparable methods in most scenarios. These results demonstrate that OWC is a useful tool that adapts to the underlying biological model for a disease by weighting appropriately genetic variants and combination of well-known gene-based tests.
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Affiliation(s)
- Jianjun Zhang
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Xiaoyu Liang
- grid.17088.360000 0001 2150 1785Department of Epidemiology and Biostatistics, Michigan State University, 909 Wilson Rd Room B601, East Lansing, MI 48824 USA
| | - Samantha Gonzales
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Jianguo Liu
- grid.266869.50000 0001 1008 957XDepartment of Mathematics, University of North Texas, 225 Avenue E, Denton, TX 76201 USA
| | - Xiaoyi Raymond Gao
- grid.261331.40000 0001 2285 7943Department of Ophthalmology and Visual Science, Department of Biomedical informatics, Division of Human Genetics, Ohio State University, 915 Olentangy River Road, Columbus, OH 43212 USA
| | - Xuexia Wang
- grid.65456.340000 0001 2110 1845Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, 11200 SW 8th street, Miami, FL 33174 USA
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Zhang L, Papachristou C, Choudhary PK, Biswas S. A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies. Hum Hered 2020; 84:240-255. [PMID: 32966977 DOI: 10.1159/000508664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 05/14/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. METHODS We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference. RESULTS We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers. CONCLUSION Our method can be helpful in detecting pathway association.
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Affiliation(s)
- Lei Zhang
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | | | - Pankaj K Choudhary
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA,
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Jiang K, Yang Z, Cui W, Su K, Ma JZ, Payne TJ, Li MD. An Exome-Wide Association Study Identifies New Susceptibility Loci for Age of Smoking Initiation in African- and European-American Populations. Nicotine Tob Res 2020; 21:707-713. [PMID: 29216386 DOI: 10.1093/ntr/ntx262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 11/28/2017] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Cigarette smoking is one of the largest causes of preventable death worldwide. This study aimed to identify susceptibility loci for age at smoking initiation (ASI) by performing an exome-wide association analysis. METHODS A total of 2510 smokers of either African-American (AA) or European-American (EA) origin were genotyped and analyzed at both the single nucleotide polymorphism (SNP) and gene levels. After removal of those SNPs with a minor allele frequency (<0.01), 48091 and 34933 SNPs for AAs and EAs, respectively, were used to conduct a SNP-based association analysis. Gene-based analyses were then performed for all SNPs examined within each gene. Further, we estimated the proportion of variance explained by all common SNPs included in the analysis. RESULTS The strongest signals were detected for SNPs rs17849904 in the pitrilysin metallopeptidase 1 gene (PITRM1) in the AA sample (p = 9.02 × 10-7) and rs34722354 in the discoidin domain of the receptor tyrosine kinase 2 gene (DDR2) in the EA sample (p = 9.74 × 10-7). Both SNPs remained significant after Bonferroni correction for the number of SNPs tested. Subsequently, the gene-based association analysis revealed a significantly associated gene, DHRS7, in the AA sample (p = 5.00 × 10-6), a gene previously implicated in nicotine metabolism. CONCLUSIONS Our study revealed two susceptibility loci for age of smoking initiation in the two ethnic samples, with the first being PITRM1 for AA smokers and the second DDR2 for EA smokers. In addition, we found DHRS7 to be a plausible candidate for ASI in the AA sample from our gene-based association analysis. IMPLICATIONS PITRM1 and DHRS7 for African-American smokers and DDR2 for European-American smokers are new candidate genes for smoking initiation. These genes represent new additions to smoking initiation, an important but less studied phenotype in nicotine dependence research.
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Affiliation(s)
- Keran Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongli Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenyan Cui
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China
| | - Kunkai Su
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China
| | - Jennie Z Ma
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Thomas J Payne
- ACT Center for Tobacco Treatment, Education and Research University of Mississippi Medical Center, Jackson, MS.,Department of Otolaryngology and Communicative Sciences, University of Mississippi Medical Center, Jackson, MS
| | - Ming D Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang University School of Medicine, Hangzhou, China.,Research Center for Air Pollution and Health, Zhejiang University, Hangzhou, China.,Institute of Neuroimmune Pharmacology, Seton Hall University, South Orange, NJ, USA
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5
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Escala-Garcia M, Abraham J, Andrulis IL, Anton-Culver H, Arndt V, Ashworth A, Auer PL, Auvinen P, Beckmann MW, Beesley J, Behrens S, Benitez J, Bermisheva M, Blomqvist C, Blot W, Bogdanova NV, Bojesen SE, Bolla MK, Børresen-Dale AL, Brauch H, Brenner H, Brucker SY, Burwinkel B, Caldas C, Canzian F, Chang-Claude J, Chanock SJ, Chin SF, Clarke CL, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Dennis J, Devilee P, Dunn JA, Dunning AM, Dwek M, Earl HM, Eccles DM, Eliassen AH, Ellberg C, Evans DG, Fasching PA, Figueroa J, Flyger H, Gago-Dominguez M, Gapstur SM, García-Closas M, García-Sáenz JA, Gaudet MM, George A, Giles GG, Goldgar DE, González-Neira A, Grip M, Guénel P, Guo Q, Haiman CA, Håkansson N, Hamann U, Harrington PA, Hiller L, Hooning MJ, Hopper JL, Howell A, Huang CS, Huang G, Hunter DJ, Jakubowska A, John EM, Kaaks R, Kapoor PM, Keeman R, Kitahara CM, Koppert LB, Kraft P, Kristensen VN, Lambrechts D, Le Marchand L, Lejbkowicz F, Lindblom A, Lubiński J, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Maurer T, Mavroudis D, Meindl A, Milne RL, Mulligan AM, Neuhausen SL, Nevanlinna H, Newman WG, Olshan AF, Olson JE, Olsson H, Orr N, Peterlongo P, Petridis C, Prentice RL, Presneau N, Punie K, Ramachandran D, Rennert G, Romero A, Sachchithananthan M, Saloustros E, Sawyer EJ, Schmutzler RK, Schwentner L, Scott C, Simard J, Sohn C, Southey MC, Swerdlow AJ, Tamimi RM, Tapper WJ, Teixeira MR, Terry MB, Thorne H, Tollenaar RAEM, Tomlinson I, Troester MA, Truong T, Turnbull C, Vachon CM, van der Kolk LE, Wang Q, Winqvist R, Wolk A, Yang XR, Ziogas A, Pharoah PDP, Hall P, Wessels LFA, Chenevix-Trench G, Bader GD, Dörk T, Easton DF, Canisius S, Schmidt MK. A network analysis to identify mediators of germline-driven differences in breast cancer prognosis. Nat Commun 2020; 11:312. [PMID: 31949161 PMCID: PMC6965101 DOI: 10.1038/s41467-019-14100-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 12/17/2019] [Indexed: 11/09/2022] Open
Abstract
Identifying the underlying genetic drivers of the heritability of breast cancer prognosis remains elusive. We adapt a network-based approach to handle underpowered complex datasets to provide new insights into the potential function of germline variants in breast cancer prognosis. This network-based analysis studies ~7.3 million variants in 84,457 breast cancer patients in relation to breast cancer survival and confirms the results on 12,381 independent patients. Aggregating the prognostic effects of genetic variants across multiple genes, we identify four gene modules associated with survival in estrogen receptor (ER)-negative and one in ER-positive disease. The modules show biological enrichment for cancer-related processes such as G-alpha signaling, circadian clock, angiogenesis, and Rho-GTPases in apoptosis.
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Affiliation(s)
- Maria Escala-Garcia
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Jean Abraham
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Cambridge Experimental Cancer Medicine Centre, Cambridge, UK
- Cambridge Breast Unit and NIHR Cambridge Biomedical Research Centre, University of Cambridge NHS Foundation Hospitals, Cambridge, UK
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Hoda Anton-Culver
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alan Ashworth
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Paul L Auer
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Päivi Auvinen
- Cancer Center, Kuopio University Hospital, Kuopio, Finland
- Institute of Clinical Medicine, Oncology, University of Eastern Finland, Kuopio, Finland
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Jonathan Beesley
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Javier Benitez
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
- Biomedical Network on Rare Diseases (CIBERER), Madrid, Spain
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russia
| | - Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
- Department of Oncology, Örebro University Hospital, Örebro, Sweden
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- International Epidemiology Institute, Rockville, MD, USA
| | - Natalia V Bogdanova
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
- N.N. Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus
| | - Stig E Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Manjeet K Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Hiltrud Brauch
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- iFIT-Cluster of Excellence, University of Tuebingen, Tuebingen, Germany
- German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Sara Y Brucker
- Department of Gynecology and Obstetrics, University of Tübingen, Tübingen, Germany
| | - Barbara Burwinkel
- Molecular Epidemiology Group, C080, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Biology of Breast Cancer, University Womens Clinic Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Carlos Caldas
- Cancer Research UK Cambridge Institute, Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Breast Cancer Programme, CRUK Cambridge Cancer Centre and NIHR Biomedical Research Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Suet-Feung Chin
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Christine L Clarke
- Westmead Institute for Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Angela Cox
- Department of Oncology and Metabolism, Sheffield Institute for Nucleic Acids (SInFoNiA), University of Sheffield, Sheffield, UK
| | - Simon S Cross
- Academic Unit of Pathology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mary B Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Joe Dennis
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Janet A Dunn
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
| | - Alison M Dunning
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Miriam Dwek
- Department of Biomedical Sciences, Faculty of Science and Technology, University of Westminster, London, UK
| | - Helena M Earl
- Cambridge Breast Unit and NIHR Cambridge Biomedical Research Centre, University of Cambridge NHS Foundation Hospitals, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Diana M Eccles
- Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Carolina Ellberg
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - D Gareth Evans
- Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Genomic Medicine, St Mary's Hospital, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK
- Cancer Research UK Edinburgh Centre, Edinburgh, UK
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark
| | - Manuela Gago-Dominguez
- Genomic Medicine Group, Galician Foundation of Genomic Medicine, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - José A García-Sáenz
- Medical Oncology Department, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Centro Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
| | - Mia M Gaudet
- Epidemiology Research Program, American Cancer Society, Atlanta, GA, USA
| | - Angela George
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - David E Goldgar
- Department of Dermatology, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Anna González-Neira
- Human Cancer Genetics Programme, Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Mervi Grip
- Department of Surgery, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Pascal Guénel
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), University Paris-Saclay, INSERM, University Paris-Sud, Villejuif, France
| | - Qi Guo
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Niclas Håkansson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Patricia A Harrington
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Louise Hiller
- Warwick Clinical Trials Unit, University of Warwick, Coventry, UK
| | - Maartje J Hooning
- Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Chiun-Sheng Huang
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Guanmengqian Huang
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - David J Hunter
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland
| | - Esther M John
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pooja Middha Kapoor
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Medicine, University of Heidelberg, Heidelberg, Germany
| | - Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Linetta B Koppert
- Department of Surgical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Diether Lambrechts
- VIB, VIB Center for Cancer Biology, Leuven, Belgium
- Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Flavio Lejbkowicz
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Arto Mannermaa
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Clinical Pathology, Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Mehdi Manoochehri
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Siranoush Manoukian
- Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano (INT), Milan, Italy
| | - Sara Margolin
- Department of Oncology, Sšdersjukhuset, Stockholm, Sweden
- Department of Clinical Science and Education, Sšdersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Maria Elena Martinez
- Moores Cancer Center, University of California San Diego, La Jolla, CA, USA
- Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Tabea Maurer
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece
| | - Alfons Meindl
- Department of Gynecology and Obstetrics, Ludwig Maximilian University of Munich, Munich, Germany
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine Program, University Health Network, Toronto, ON, Canada
| | - Susan L Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - William G Newman
- Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
- Genomic Medicine, St Mary's Hospital, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Andrew F Olshan
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Håkan Olsson
- Department of Cancer Epidemiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Nick Orr
- Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Ireland, UK
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM - the FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology, Milan, Italy
| | - Christos Petridis
- Research Oncology, Guy's Hospital, King's College London, London, UK
| | - Ross L Prentice
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nadege Presneau
- Department of Biomedical Sciences, Faculty of Science and Technology, University of Westminster, London, UK
| | - Kevin Punie
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | | | - Gad Rennert
- Carmel Medical Center and Technion Faculty of Medicine, Clalit National Cancer Control Center, Haifa, Israel
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain
| | | | | | - Elinor J Sawyer
- Research Oncology, Guy's Hospital, King's College London, London, UK
| | - Rita K Schmutzler
- Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Lukas Schwentner
- Department of Gynaecology and Obstetrics, University Hospital Ulm, Ulm, Germany
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Jacques Simard
- Genomics Center, Research Center, Centre Hospitalier Universitaire de Québec - Université Laval, Québec City, QC, Canada
| | - Christof Sohn
- National Center for Tumor Diseases, University of Heidelberg, Heidelberg, Germany
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
- Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, Porto, Portugal
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Heather Thorne
- Peter MacCallum Cancer Center, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
| | - Ian Tomlinson
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
- Wellcome Trust Centre for Human Genetics and Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Melissa A Troester
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Thérèse Truong
- Cancer & Environment Group, Center for Research in Epidemiology and Population Health (CESP), University Paris-Saclay, INSERM, University Paris-Sud, Villejuif, France
| | - Clare Turnbull
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Lizet E van der Kolk
- Family Cancer Clinic, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Qin Wang
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Robert Winqvist
- Biocenter Oulu, Cancer and Translational Medicine Research Unit, Laboratory of Cancer Genetics and Tumor Biology, University of Oulu, Oulu, Finland
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Xiaohong R Yang
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Argyrios Ziogas
- Department of Epidemiology, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA
| | - Paul D P Pharoah
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Sšdersjukhuset, Stockholm, Sweden
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Faculty of EEMCS, Delft University of Technology, Delft, The Netherlands
| | - Georgia Chenevix-Trench
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- The Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany
| | - Douglas F Easton
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sander Canisius
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Molecular Carcinogenesis, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
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6
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Li M, Jiang L, Mak TSH, Kwan JSH, Xue C, Chen P, Leung HCM, Cui L, Li T, Sham PC. A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia. Bioinformatics 2019; 35:628-635. [PMID: 30101339 DOI: 10.1093/bioinformatics/bty682] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 06/27/2018] [Accepted: 08/06/2018] [Indexed: 02/05/2023] Open
Abstract
MOTIVATION It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes. RESULTS We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independently associated genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant P-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains. AVAILABILITY AND IMPLEMENTATION The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Miaoxin Li
- Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.,The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.,Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China.,State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.,Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, Hong Kong, China
| | - Lin Jiang
- Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.,The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Timothy Shin Heng Mak
- The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | | | - Chao Xue
- Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
| | - Peikai Chen
- The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.,School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Henry Chi-Ming Leung
- Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Liqian Cui
- The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Li
- The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China
| | - Pak Chung Sham
- The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.,Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China.,State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China
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7
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Abstract
Health care provision is changing, and so is the information we use to guide decisions related to patient care. Increasingly, health practitioners will need to deal with genetics and 'big data' in the context of clinical practice. Indeed, commercial packages for consumer genetic testing are already widely available, and devices enabling self-monitoring of health are in daily use by many of our patients. "Precision health" (distinct from "precision medicine") provides a model, which allows us to bring our genome together with our external environment (lifestyles, societal influences etc.) and eventually, our transient internal environment (reflected by 'omics'), to optimise disease prevention and care. Such advancements have given rise to a need for primary health care clinicians to understand basic genetic and precision health concepts. This editorial meets this need, serving as a primer by providing the following: an introduction to current primary health challenges; description of the key elements of the precision health model; an overview of basic genetic, and associated research concepts; a snapshot of some clinically pertinent research in the context of precision health; and a brief discussion of challenges and future directions.
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Affiliation(s)
- Cameron Dickson
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, GPO Box 2471, Adelaide, South Australia 5001, Australia.
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, GPO Box 2471, Adelaide, South Australia 5001, Australia; Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, UK; South Australian Health and Medical Research Institute, PO Box 11060, Adelaide, South Australia 5001, Australia.
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8
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Zilbermint M, Gaye A, Berthon A, Hannah‐Shmouni F, Faucz FR, Lodish MB, Davis AR, Gibbons GH, Stratakis CA. ARMC 5 Variants and Risk of Hypertension in Blacks: MH- GRID Study. J Am Heart Assoc 2019; 8:e012508. [PMID: 31266387 PMCID: PMC6662143 DOI: 10.1161/jaha.119.012508] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 05/29/2019] [Indexed: 12/31/2022]
Abstract
Background We recently found that ARMC 5 variants may be associated with primary aldosteronism in blacks. We investigated a cohort from the MH - GRID (Minority Health Genomics and Translational Research Bio-Repository Database) and tested the association between ARMC 5 variants and blood pressure in black s. Methods and Results Whole exome sequencing data of 1377 black s were analyzed. Target single-variant and gene-based association analyses of hypertension were performed for ARMC 5, and replicated in a subset of 3015 individuals of African descent from the UK Biobank cohort. Sixteen rare variants were significantly associated with hypertension ( P=0.0402) in the gene-based (optimized sequenced kernel association test) analysis; the 16 and one other, rs116201073, together, showed a strong association ( P=0.0003) with blood pressure in this data set. The presence of the rs116201073 variant was associated with lower blood pressure. We then used human embryonic kidney 293 and adrenocortical H295R cells transfected with an ARMC 5 construct containing rs116201073 (c.*920T>C). The latter was common in both the discovery ( MH - GRID ) and replication ( UK Biobank) data and reached statistical significance ( P=0.044 [odds ratio, 0.7] and P=0.007 [odds ratio, 0.76], respectively). The allele carrying rs116201073 increased levels of ARMC5 mRNA , consistent with its protective effect in the epidemiological data. Conclusions ARMC 5 shows an association with hypertension in black s when rare variants within the gene are considered. We also identified a protective variant of the ARMC 5 gene with an effect on ARMC 5 expression confirmed in vitro. These results extend our previous report of ARMC 5's possible involvement in the determination of blood pressure in blacks.
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Affiliation(s)
- Mihail Zilbermint
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
- Division of Endocrinology, Diabetes, and MetabolismJohns Hopkins University School of MedicineBaltimoreMD
- Johns Hopkins Community Physicians at Suburban HospitalBethesdaMD
- Johns Hopkins University Carey Business SchoolBaltimoreMD
| | - Amadou Gaye
- Genomics of Metabolic, Cardiovascular and Inflammatory Disease Branch, Cardiovascular SectionNational Human Genome Research InstituteBethesdaMD
| | - Annabel Berthon
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
| | - Fady Hannah‐Shmouni
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
| | - Fabio R. Faucz
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
| | - Maya B. Lodish
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
| | - Adam R. Davis
- Technological Research and InnovationUniformed Services UniversityBethesdaMD
| | - Gary H. Gibbons
- Genomics of Metabolic, Cardiovascular and Inflammatory Disease Branch, Cardiovascular SectionNational Human Genome Research InstituteBethesdaMD
- National Heart, Lung, and Blood InstituteBethesdaMD
| | - Constantine A. Stratakis
- Section on Endocrinology and GeneticsEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMD
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9
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A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence. J Hum Genet 2019; 64:781-787. [PMID: 31165785 DOI: 10.1038/s10038-019-0623-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 04/28/2019] [Accepted: 05/15/2019] [Indexed: 01/10/2023]
Abstract
Genome-wide association studies (GWASs) have identified >20 genetic loci associated with human intelligence. However, due to correlations between the trait-associated SNPs, only a few of the loci are confirmed to have a true biological effect. In order to distinguish the SNPs that have a causal effect on human intelligence, we must eliminate the noise from the high degree of linkage disequilibrium that persists throughout the genome. In this study, we apply a novel PAINTOR fine-mapping method, which uses a Bayesian approach to determine the SNPs with the highest probability of causality. This technique incorporates the GWAS summary statistics, linkage disequilibrium structure, and functional annotations to compute the posterior probability of causality for all SNPs in the GWAS-associated regions. We found five SNPs (rs6002620, rs41352752, rs6568547, rs138592330, and rs28371699) with a high probability of causality, three of which have posterior probabilities >0.60. The SNP rs6002620 (NDUFA6), which is involved in mitochondrial function, has the highest likelihood of causality. These findings provide important insight into the genetic determinants contributing to human intelligence.
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10
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Ho EYK, Cao Q, Gu M, Chan RWL, Wu Q, Gerstein M, Yip KY. Shaping the nebulous enhancer in the era of high-throughput assays and genome editing. Brief Bioinform 2019; 21:836-850. [PMID: 30895290 DOI: 10.1093/bib/bbz030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 02/15/2019] [Accepted: 02/26/2019] [Indexed: 01/22/2023] Open
Abstract
Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.
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Affiliation(s)
| | - Qin Cao
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Mengting Gu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
| | - Ricky Wai-Lun Chan
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Qiong Wu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.,Program in Computational Biology and Bioinformatics.,Department of Computer Science, Yale University, New Haven, Connecticut, USA
| | - Kevin Y Yip
- Department of Biomedical Engineering.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,Hong Kong Bioinformatics Centre.,CUHK-BGI Innovation Institute of Trans-omics.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong
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11
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Guinot F, Szafranski M, Ambroise C, Samson F. Learning the optimal scale for GWAS through hierarchical SNP aggregation. BMC Bioinformatics 2018; 19:459. [PMID: 30497371 PMCID: PMC6267789 DOI: 10.1186/s12859-018-2475-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 11/09/2018] [Indexed: 11/16/2022] Open
Abstract
Background Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual’s genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs. Results We propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and group-based approaches on both synthetic and real GWAS data. Conclusion We show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions.
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Affiliation(s)
- Florent Guinot
- UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France. .,BIOptimize, Reims, 51000, France.
| | - Marie Szafranski
- UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France
| | - Christophe Ambroise
- UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France.,UMR MIA-Paris - AgroParisTech, INRA, Université Paris-Saclay, Paris, 75005, France
| | - Franck Samson
- UMR 8071 LaMME - UEVE, CNRS, ENSIIE, USC INRA, 23 bd de France, Evry, 91000, France
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12
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Epigenetic dysregulation of naive CD4+ T-cell activation genes in childhood food allergy. Nat Commun 2018; 9:3308. [PMID: 30120223 PMCID: PMC6098117 DOI: 10.1038/s41467-018-05608-4] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 06/05/2018] [Indexed: 12/31/2022] Open
Abstract
Food allergy poses a significant clinical and public health burden affecting 2–10% of infants. Using integrated DNA methylation and transcriptomic profiling, we found that polyclonal activation of naive CD4+ T cells through the T cell receptor results in poorer lymphoproliferative responses in children with immunoglobulin E (IgE)-mediated food allergy. Reduced expression of cell cycle-related targets of the E2F and MYC transcription factor networks, and remodeling of DNA methylation at metabolic (RPTOR, PIK3D, MAPK1, FOXO1) and inflammatory genes (IL1R, IL18RAP, CD82) underpins this suboptimal response. Infants who fail to resolve food allergy in later childhood exhibit cumulative increases in epigenetic disruption at T cell activation genes and poorer lymphoproliferative responses compared to children who resolved food allergy. Our data indicate epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex, and likely reflects pathways modified by gene–environment interactions in food allergy. Immunoglobulin E (IgE)-mediated food allergy is a major issue that affects 2–10% of infants. Here the authors study the epigenetic regulation of the naive CD4+ T cell activation response among children with IgE-mediated food allergy finding epigenetic dysregulation in the early stages of signal transduction through the T cell receptor complex.
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13
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Husser D, Büttner P, Stübner D, Ueberham L, Platonov PG, Dinov B, Arya A, Hindricks G, Bollmann A. PR Interval Associated Genes, Atrial Remodeling and Rhythm Outcome of Catheter Ablation of Atrial Fibrillation-A Gene-Based Analysis of GWAS Data. Front Genet 2018; 8:224. [PMID: 29312445 PMCID: PMC5742186 DOI: 10.3389/fgene.2017.00224] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 12/11/2017] [Indexed: 01/25/2023] Open
Abstract
Background: PR interval prolongation has recently been shown to associate with advanced left atrial remodeling and atrial fibrillation (AF) recurrence after catheter ablation. While different genome-wide association studies (GWAS) have implicated 13 loci to associate with the PR interval as an AF endophenotype their subsequent associations with AF remodeling and response to catheter ablation are unknown. Here, we perform a gene-based analysis of GWAS data to test the hypothesis that PR interval candidate genes also associate with left atrial remodeling and arrhythmia recurrence following AF catheter ablation. Methods and Results: Samples from 660 patients with paroxysmal (n = 370) or persistent AF (n = 290) undergoing AF catheter ablation were genotyped for ~1,000,000 SNPs. Gene-based association was investigated using VEGAS (versatile gene-based association study). Among the 13 candidate genes, SLC8A1, MEIS1, ITGA9, SCN5A, and SOX5 associated with the PR interval. Of those, ITGA9 and SOX5 were significantly associated with left atrial low voltage areas and left atrial diameter and subsequently with AF recurrence after radiofrequency catheter ablation. Conclusion: This study suggests contributions of ITGA9 and SOX5 to AF remodeling expressed as PR interval prolongation, low voltage areas and left atrial dilatation and subsequently to response to catheter ablation. Future and larger studies are necessary to replicate and apply these findings with the aim of designing AF pathophysiology-based multi-locus risk scores.
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Affiliation(s)
- Daniela Husser
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Petra Büttner
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Dorian Stübner
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Laura Ueberham
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
| | - Pyotr G Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Borislav Dinov
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Arash Arya
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig, Leipzig University, Leipzig, Germany.,Leipzig Heart Institute, Leipzig, Germany
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14
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Hayden LP, Cho MH, McDonald MLN, Crapo JD, Beaty TH, Silverman EK, Hersh CP. Susceptibility to Childhood Pneumonia: A Genome-Wide Analysis. Am J Respir Cell Mol Biol 2017; 56:20-28. [PMID: 27508494 DOI: 10.1165/rcmb.2016-0101oc] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Previous studies have indicated that in adult smokers, a history of childhood pneumonia is associated with reduced lung function and chronic obstructive pulmonary disease. There have been few previous investigations using genome-wide association studies to investigate genetic predisposition to pneumonia. This study aims to identify the genetic variants associated with the development of pneumonia during childhood and over the course of the lifetime. Study subjects included current and former smokers with and without chronic obstructive pulmonary disease participating in the COPDGene Study. Pneumonia was defined by subject self-report, with childhood pneumonia categorized as having the first episode at <16 years. Genome-wide association studies for childhood pneumonia (843 cases, 9,091 control subjects) and lifetime pneumonia (3,766 cases, 5,659 control subjects) were performed separately in non-Hispanic whites and African Americans. Non-Hispanic white and African American populations were combined in the meta-analysis. Top genetic variants from childhood pneumonia were assessed in network analysis. No single-nucleotide polymorphisms reached genome-wide significance, although we identified potential regions of interest. In the childhood pneumonia analysis, this included variants in NGR1 (P = 6.3 × 10-8), PAK6 (P = 3.3 × 10-7), and near MATN1 (P = 2.8 × 10-7). In the lifetime pneumonia analysis, this included variants in LOC339862 (P = 8.7 × 10-7), RAPGEF2 (P = 8.4 × 10-7), PHACTR1 (P = 6.1 × 10-7), near PRR27 (P = 4.3 × 10-7), and near MCPH1 (P = 2.7 × 10-7). Network analysis of the genes associated with childhood pneumonia included top networks related to development, blood vessel morphogenesis, muscle contraction, WNT signaling, DNA damage, apoptosis, inflammation, and immune response (P ≤ 0.05). We have identified genes potentially associated with the risk of pneumonia. Further research will be required to confirm these associations and to determine biological mechanisms. CLINICAL TRIAL REGISTRATION NCT00608764.
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Affiliation(s)
- Lystra P Hayden
- 1 Division of Respiratory Diseases, Boston Children's Hospital, Boston, Massachusetts.,2 Channing Division of Network Medicine and
| | - Michael H Cho
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | - Terri H Beaty
- 5 Johns Hopkins University, Bloomberg School of Public Health, Baltimore, Maryland
| | - Edwin K Silverman
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Craig P Hersh
- 2 Channing Division of Network Medicine and.,3 Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
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15
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Todorova VK, Makhoul I, Dhakal I, Wei J, Stone A, Carter W, Owen A, Klimberg VS. Polymorphic Variations Associated With Doxorubicin-Induced Cardiotoxicity in Breast Cancer Patients. Oncol Res 2017; 25:1223-1229. [PMID: 28256194 PMCID: PMC7841056 DOI: 10.3727/096504017x14876245096439] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Doxorubicin (DOX) is a commonly used antineoplastic agent for the treatment of various malignancies, and its use is associated with unpredictable cardiotoxicity. Susceptibility to DOX cardiotoxicity is largely patient dependent, suggesting genetic predisposition. We have previously found that individual sensitivity to DOX cardiotoxicity was associated with differential expression of genes implicated in inflammatory response and immune trafficking, which was consistent with the increasing number of reports highlighting the important role of human leukocyte antigen (HLA) complex polymorphism in hypersensitivity to drug toxicity. This pilot study aimed to investigate DNA from patients treated with DOX-based chemotherapy for breast cancer and to correlate the results with the risk for DOX-associated cardiotoxicity. We have identified 18 SNPs in nine genes in the HLA region (NFKBIL1, TNF-α, ATP6V1G2-DDX39B, MSH5, MICA, LTA, BAT1, and NOTCH4) and in the psoriasis susceptibility region of HLA-C as potential candidates for association with DOX cardiotoxicity. These results, albeit preliminary and involving a small number of patients, are consistent with reports showing the presence of susceptibility loci within the HLA gene region for several inflammatory and autoimmune diseases, and with our previous findings indicating that the increased sensitivity to DOX cardiotoxicity was associated with dysregulation of genes implicated both in inflammation and autoimmune disorders.
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16
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Kar SP, Adler E, Tyrer J, Hazelett D, Anton-Culver H, Bandera EV, Beckmann MW, Berchuck A, Bogdanova N, Brinton L, Butzow R, Campbell I, Carty K, Chang-Claude J, Cook LS, Cramer DW, Cunningham JM, Dansonka-Mieszkowska A, Doherty JA, Dörk T, Dürst M, Eccles D, Fasching PA, Flanagan J, Gentry-Maharaj A, Glasspool R, Goode EL, Goodman MT, Gronwald J, Heitz F, Hildebrandt MAT, Høgdall E, Høgdall CK, Huntsman DG, Jensen A, Karlan BY, Kelemen LE, Kiemeney LA, Kjaer SK, Kupryjanczyk J, Lambrechts D, Levine DA, Li Q, Lissowska J, Lu KH, Lubiński J, Massuger LFAG, McGuire V, McNeish I, Menon U, Modugno F, Monteiro AN, Moysich KB, Ness RB, Nevanlinna H, Paul J, Pearce CL, Pejovic T, Permuth JB, Phelan C, Pike MC, Poole EM, Ramus SJ, Risch HA, Rossing MA, Salvesen HB, Schildkraut JM, Sellers TA, Sherman M, Siddiqui N, Sieh W, Song H, Southey M, Terry KL, Tworoger SS, Walsh C, Wentzensen N, Whittemore AS, Wu AH, Yang H, Zheng W, Ziogas A, Freedman ML, Gayther SA, Pharoah PDP, Lawrenson K. Enrichment of putative PAX8 target genes at serous epithelial ovarian cancer susceptibility loci. Br J Cancer 2017; 116:524-535. [PMID: 28103614 PMCID: PMC5318969 DOI: 10.1038/bjc.2016.426] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Revised: 11/23/2016] [Accepted: 11/29/2016] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified 18 loci associated with serous ovarian cancer (SOC) susceptibility but the biological mechanisms driving these findings remain poorly characterised. Germline cancer risk loci may be enriched for target genes of transcription factors (TFs) critical to somatic tumorigenesis. METHODS All 615 TF-target sets from the Molecular Signatures Database were evaluated using gene set enrichment analysis (GSEA) and three GWAS for SOC risk: discovery (2196 cases/4396 controls), replication (7035 cases/21 693 controls; independent from discovery), and combined (9627 cases/30 845 controls; including additional individuals). RESULTS The PAX8-target gene set was ranked 1/615 in the discovery (PGSEA<0.001; FDR=0.21), 7/615 in the replication (PGSEA=0.004; FDR=0.37), and 1/615 in the combined (PGSEA<0.001; FDR=0.21) studies. Adding other genes reported to interact with PAX8 in the literature to the PAX8-target set and applying an alternative to GSEA, interval enrichment, further confirmed this association (P=0.006). Fifteen of the 157 genes from this expanded PAX8 pathway were near eight loci associated with SOC risk at P<10-5 (including six with P<5 × 10-8). The pathway was also associated with differential gene expression after shRNA-mediated silencing of PAX8 in HeyA8 (PGSEA=0.025) and IGROV1 (PGSEA=0.004) SOC cells and several PAX8 targets near SOC risk loci demonstrated in vitro transcriptomic perturbation. CONCLUSIONS Putative PAX8 target genes are enriched for common SOC risk variants. This finding from our agnostic evaluation is of particular interest given that PAX8 is well-established as a specific marker for the cell of origin of SOC.
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Affiliation(s)
- Siddhartha P Kar
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Emily Adler
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Jonathan Tyrer
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Dennis Hazelett
- Bioinformatics and Computational Biology Research Center, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Hoda Anton-Culver
- Department of Epidemiology, Director of Genetic Epidemiology Research Institute, UCI Center for Cancer Genetics Research & Prevention, School of Medicine, University of California Irvine, Irvine, CA 92697, USA
| | - Elisa V Bandera
- Cancer Prevention and Control Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Matthias W Beckmann
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, Erlangen 91054, Germany
| | - Andrew Berchuck
- Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC 27710, USA
| | - Natalia Bogdanova
- Radiation Oncology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Louise Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ralf Butzow
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki 00100, Finland
| | - Ian Campbell
- Cancer Genetics Laboratory, Research Division, Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, VIC 3002, Australia
- Department of Pathology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Karen Carty
- The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, UK
| | - Jenny Chang-Claude
- German Cancer Research Center, Division of Cancer Epidemiology, Heidelberg 69120, Germany
- University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg 20246, Germany
| | - Linda S Cook
- Division of Epidemiology and Biostatistics, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA
| | - Daniel W Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Agnieszka Dansonka-Mieszkowska
- Department of Pathology, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw 02-781, Poland
| | - Jennifer Anne Doherty
- Department of Epidemiology, The Geisel School of Medicine—at Dartmouth, Hanover, NH 03756, USA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover 30625, Germany
| | - Matthias Dürst
- Department of Gynecology, Jena-University Hospital-Friedrich Schiller University, Jena 07737, Germany
| | - Diana Eccles
- Faculty of Medicine, University of Southampton, Southampton SO16 5YA, UK
| | - Peter A Fasching
- University Hospital Erlangen, Department of Gynecology and Obstetrics, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen Nuremberg, Universitaetsstrasse 21-23, Erlangen 91054, Germany
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - James Flanagan
- Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, UK
| | - Aleksandra Gentry-Maharaj
- Department of Women's Cancer, Institute for Women's Health, University College London, London W1T 7DN, UK
| | | | - Ellen L Goode
- Department of Health Science Research, Division of Epidemiology, Mayo Clinic, Rochester, MI 55905, USA
| | - Marc T Goodman
- Cancer Prevention and Control, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Community and Population Health Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jacek Gronwald
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 70-001, Poland
| | - Florian Heitz
- Department of Gynecology and Gynecologic Oncology, Kliniken Essen-Mitte/ Evang. Huyssens-Stiftung/ Knappschaft GmbH, Essen 45136, Germany
- Department of Gynecology and Gynecologic Oncology, Dr Horst Schmidt Kliniken Wiesbaden, Wiesbaden 65199, Germany
| | - Michelle A T Hildebrandt
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Estrid Høgdall
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen 2100, Denmark
- Molecular Unit, Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen 1165, Denmark
| | - Claus K Høgdall
- The Juliane Marie Centre, Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark
| | - David G Huntsman
- British Columbia's Ovarian Cancer Research (OVCARE) Program, Vancouver General Hospital, BC Cancer Agency and University of British Columbia, Vancouver, BC V5Z 1L3, Canada
- Departments of Pathology and Laboratory Medicine and Obstetrics and Gynaecology, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
- Department of Molecular Oncology, BC Cancer Agency Research Centre, Vancouver, BC V5Z 1L3, Canada
| | - Allan Jensen
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen 2100, Denmark
| | - Beth Y Karlan
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Linda E Kelemen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29435, USA
| | - Lambertus A Kiemeney
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen 6500 HB, The Netherlands
| | - Susanne K Kjaer
- Department of Virus, Lifestyle and Genes, Danish Cancer Society Research Center, Copenhagen 2100, Denmark
- Department of Gynaecology, Rigshospitalet, University of Copenhagen, Copenhagen 2100, Denmark
| | - Jolanta Kupryjanczyk
- Department of Pathology, The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw 02-781, Poland
| | - Diether Lambrechts
- Vesalius Research Center, VIB, Leuven 3000, Belgium
- Laboratory for Translational Genetics, Department of Oncology, University of Leuven 3000, Belgium
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Qiyuan Li
- Department of Medical Oncology, The Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Medical College of Xiamen University, Xiamen 361102, China
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw 02-781, Poland
| | - Karen H Lu
- Department of Gynecologic Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jan Lubiński
- International Hereditary Cancer Center, Department of Genetics and Pathology, Pomeranian Medical University, Szczecin 70-001, Poland
| | - Leon F A G Massuger
- Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Department of Gynaecology, Nijmegen 6500 HB, The Netherlands
| | - Valerie McGuire
- Department of Health Research and Policy—Epidemiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Iain McNeish
- Institute of Cancer Sciences, University of Glasgow, Wolfson Wohl Cancer Research Centre, Beatson Institute for Cancer Research, Glasgow G12 0YN, UK
| | - Usha Menon
- Department of Women's Cancer, Institute for Women's Health, University College London, London W1T 7DN, UK
| | - Francesmary Modugno
- Division of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA 15213, USA
- Ovarian Cancer Center of Excellence, Womens Cancer Research Program, Magee-Womens Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, PA 15213, USA
| | - Alvaro N Monteiro
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
| | - Roberta B Ness
- The University of Texas School of Public Health, Houston, TX 77030, USA
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki 00100, Finland
| | - James Paul
- The Beatson West of Scotland Cancer Centre, Glasgow G12 0YN, UK
| | - Celeste L Pearce
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Tanja Pejovic
- Department of Obstetrics & Gynecology, Oregon Health & Science University, Portland, OR 97239, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jennifer B Permuth
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Catherine Phelan
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Malcolm C Pike
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA
| | - Elizabeth M Poole
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA
| | - Susan J Ramus
- Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
| | - Harvey A Risch
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT 06510, USA
| | - Mary Anne Rossing
- Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98109, USA
| | - Helga B Salvesen
- Department of Gynecology and Obstetrics, Haukeland University Horpital, Bergen 5058, Norway
- Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen 5058, Norway
| | - Joellen M Schildkraut
- Department of Community and Family Medicine, Duke University Medical Center, Durham, NC 27710, USA
- Cancer Control and Population Sciences, Duke Cancer Institute, Durham, NC 27710, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Mark Sherman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nadeem Siddiqui
- Department of Gynaecological Oncology, Glasgow Royal Infirmary, Glasgow G4 0SF, UK
| | - Weiva Sieh
- Department of Health Research and Policy—Epidemiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Honglin Song
- Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Melissa Southey
- Genetic Epidemiology Laboratory, Department of Pathology, The University of Melbourne, Melbourne, VIC 3002, Australia
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital, Boston, MA 02215, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Shelley S Tworoger
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA
| | - Christine Walsh
- Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Alice S Whittemore
- Department of Health Research and Policy—Epidemiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA
| | - Hannah Yang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Wei Zheng
- Division of Epidemiology, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center Medicine, Nashville, TN 37232, USA
| | - Argyrios Ziogas
- Department of Epidemiology, University of California Irvine, Irvine, CA 92697, USA
| | - Matthew L Freedman
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- The Eli and Edythe L. Broad Institute, Cambridge, MA 02142, USA
| | - Simon A Gayther
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
- Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Cambridge CB1 8RN, UK
| | - Kate Lawrenson
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
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17
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Yoo YJ, Sun L, Poirier JG, Paterson AD, Bull SB. Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure. Genet Epidemiol 2016; 41:108-121. [PMID: 27885705 PMCID: PMC5245123 DOI: 10.1002/gepi.22024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 05/25/2016] [Accepted: 09/27/2016] [Indexed: 11/21/2022]
Abstract
By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.
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Affiliation(s)
- Yun Joo Yoo
- Department of Mathematics Education, Seoul National University, Seoul, South Korea.,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
| | - Lei Sun
- Department of Statistical Sciences, University of Toronto, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Julia G Poirier
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - Andrew D Paterson
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.,Program in Genetics and Genome Biology, Hospital for Sick Children Research Institute, Toronto, Canada
| | - Shelley B Bull
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.,Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
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18
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Hanson C, Cairns J, Wang L, Sinha S. Computational discovery of transcription factors associated with drug response. THE PHARMACOGENOMICS JOURNAL 2016; 16:573-582. [PMID: 26503816 PMCID: PMC4848185 DOI: 10.1038/tpj.2015.74] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 08/04/2015] [Accepted: 08/07/2015] [Indexed: 02/01/2023]
Abstract
This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF-treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing.
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Affiliation(s)
- C Hanson
- Department of Computer Science, University of Illinois at Urbana–Champaign, Urbana, IL, USA
| | - J Cairns
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - L Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - S Sinha
- Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana–Champaign, Urbana, IL, USA
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19
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Wang L, Salinas YD, DeWan AT. Gene-based analysis identified the gene ZNF248 is associated with late-onset asthma in African Americans. Ann Allergy Asthma Immunol 2016; 117:50-55.e2. [PMID: 27238579 PMCID: PMC5085297 DOI: 10.1016/j.anai.2016.05.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2016] [Revised: 04/28/2016] [Accepted: 05/10/2016] [Indexed: 11/15/2022]
Abstract
BACKGROUND Late-onset asthma (LOA) has distinct characteristics and its pathogenesis might rely on unique pathways. Although current studies are focused primarily on childhood asthma, more research is needed to show the mechanisms underlying LOA. OBJECTIVE To conduct genomewide association analysis and gene-based analysis to identify single-nucleotide polymorphisms and genes associated with LOA. METHODS The Women's Health Initiative (WHI) observational cohort and the Multi-Ethnic Study of Atherosclerosis (MESA) were used to identify subjects with LOA. The association between LOA and body mass index and smoking was evaluated. In the discovery stage of the genetic analysis, 1,218 African American subjects from WHI with genotype data (271 cases and 947 controls) were used for single-nucleotide polymorphism and gene-based association analyses. Significant or suggestive results were subsequently investigated in an independent African American population from MESA (38 cases and 806 controls). RESULTS In WHI, the relative odds for LOA in obese vs normal-weight subjects was 2.55 (95% confidence interval 1.74-3.76). Ever smokers also had greater odds for LOA compared with never smokers (odds ratio 1.59, 95% confidence interval 1.21-2.09). The same trends were observed in MESA. In WHI, 6 single-nucleotide polymorphisms were associated with LOA at a genomewide-suggestive significance level (P < 1.0 × 10(-5)). The gene ZNF248 was associated with LOA and reached genomewide significance (P = 4.0 × 10(-7)). In MESA, the association between ZNF248 and LOA was successfully replicated (P = .015). CONCLUSION Smoking and obesity are risk factors for LOA. ZNF248 confers increased susceptibility to LOA in African Americans.
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Affiliation(s)
- Leyao Wang
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Yasmmyn D Salinas
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut
| | - Andrew T DeWan
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut.
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20
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Kwak IY, Pan W. Adaptive gene- and pathway-trait association testing with GWAS summary statistics. Bioinformatics 2016; 32:1178-84. [PMID: 26656570 PMCID: PMC5860182 DOI: 10.1093/bioinformatics/btv719] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Revised: 11/24/2015] [Accepted: 11/29/2015] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Gene- and pathway-based analyses offer a useful alternative and complement to the usual single SNP-based analysis for GWAS. On the other hand, most existing gene- and pathway-based tests are not highly adaptive, and/or require the availability of individual-level genotype and phenotype data. It would be desirable to have highly adaptive tests applicable to summary statistics for single SNPs. This has become increasingly important given the popularity of large-scale meta-analyses of multiple GWASs and the practical availability of either single GWAS or meta-analyzed GWAS summary statistics for single SNPs. RESULTS We extend two adaptive tests for gene- and pathway-level association with a univariate trait to the case with GWAS summary statistics without individual-level genotype and phenotype data. We use the WTCCC GWAS data to evaluate and compare the proposed methods and several existing methods. We further illustrate their applications to a meta-analyzed dataset to identify genes and pathways associated with blood pressure, demonstrating the potential usefulness of the proposed methods. The methods are implemented in R package aSPU, freely and publicly available. AVAILABILITY AND IMPLEMENTATION https://cran.r-project.org/web/packages/aSPU/ CONTACT: weip@biostat.umn.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Il-Youp Kwak
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
| | - Wei Pan
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA
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21
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Boggis EM, Milo M, Walters K. eQuIPS: eQTL Analysis Using Informed Partitioning of SNPs - A Fully Bayesian Approach. Genet Epidemiol 2016; 40:273-83. [DOI: 10.1002/gepi.21961] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 12/18/2015] [Accepted: 12/18/2015] [Indexed: 11/11/2022]
Affiliation(s)
- E. M. Boggis
- School of Mathematics and Statistics; University of Sheffield; Sheffield United Kingdom
| | - M. Milo
- Department of Biomedical Science; University of Sheffield; Sheffield United Kingdom
| | - K. Walters
- School of Mathematics and Statistics; University of Sheffield; Sheffield United Kingdom
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22
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Hägg S, Ganna A, Van Der Laan SW, Esko T, Pers TH, Locke AE, Berndt SI, Justice AE, Kahali B, Siemelink MA, Pasterkamp G, Strachan DP, Speliotes EK, North KE, Loos RJF, Hirschhorn JN, Pawitan Y, Ingelsson E. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity. Hum Mol Genet 2015; 24:6849-60. [PMID: 26376864 PMCID: PMC4643645 DOI: 10.1093/hmg/ddv379] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Accepted: 09/10/2015] [Indexed: 11/12/2022] Open
Abstract
To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.
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Affiliation(s)
- Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, SE-751 41 Uppsala, Sweden
| | - Andrea Ganna
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sander W Van Der Laan
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Tonu Esko
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Estonian Genome Center, University of Tartu, Tartu 51010, Estonia
| | - Tune H Pers
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Adam E Locke
- Center for Statistical Genetics, Department of Biostatistics
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Bratati Kahali
- Department of Internal Medicine, Division of Gastroenterology, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Marten A Siemelink
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Gerard Pasterkamp
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands, Laboratory of Clinical Chemistry and Hematology, Division Laboratories & Pharmacy, UMC Utrecht, Utrecht, The Netherlands
| | | | - Elizabeth K Speliotes
- Department of Internal Medicine, Division of Gastroenterology, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kari E North
- Department of Epidemiology, Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 0QQ, UK, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA, The Genetics of Obesity and Related Metabolic Traits Program, The Mindich Child Health and Development Institute
| | - Joel N Hirschhorn
- Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Erik Ingelsson
- Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, SE-751 41 Uppsala, Sweden, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK and Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
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23
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Wang C, Kao WH, Hsiao CK. Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies. PLoS One 2015; 10:e0135918. [PMID: 26302001 PMCID: PMC4547758 DOI: 10.1371/journal.pone.0135918] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Accepted: 07/28/2015] [Indexed: 11/27/2022] Open
Abstract
The availability of high-throughput genomic data has led to several challenges in recent genetic association studies, including the large number of genetic variants that must be considered and the computational complexity in statistical analyses. Tackling these problems with a marker-set study such as SNP-set analysis can be an efficient solution. To construct SNP-sets, we first propose a clustering algorithm, which employs Hamming distance to measure the similarity between strings of SNP genotypes and evaluates whether the given SNPs or SNP-sets should be clustered. A dendrogram can then be constructed based on such distance measure, and the number of clusters can be determined. With the resulting SNP-sets, we next develop an association test HDAT to examine susceptibility to the disease of interest. This proposed test assesses, based on Hamming distance, whether the similarity between a diseased and a normal individual differs from the similarity between two individuals of the same disease status. In our proposed methodology, only genotype information is needed. No inference of haplotypes is required, and SNPs under consideration do not need to locate in nearby regions. The proposed clustering algorithm and association test are illustrated with applications and simulation studies. As compared with other existing methods, the clustering algorithm is faster and better at identifying sets containing SNPs exerting a similar effect. In addition, the simulation studies demonstrated that the proposed test works well for SNP-sets containing a large proportion of neutral SNPs. Furthermore, employing the clustering algorithm before testing a large set of data improves the knowledge in confining the genetic regions for susceptible genetic markers.
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Affiliation(s)
- Charlotte Wang
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 100, Taiwan
| | - Wen-Hsin Kao
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 100, Taiwan
| | - Chuhsing Kate Hsiao
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, 100, Taiwan
- Bioinformatics and Biostatistics Core, Division of Genomic Medicine, Research Center for Medical Excellence, National Taiwan University, Taipei, 100, Taiwan
- Department of Public Health, National Taiwan University, Taipei, 100, Taiwan
- * E-mail:
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24
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Ferrari R, Grassi M, Salvi E, Borroni B, Palluzzi F, Pepe D, D'Avila F, Padovani A, Archetti S, Rainero I, Rubino E, Pinessi L, Benussi L, Binetti G, Ghidoni R, Galimberti D, Scarpini E, Serpente M, Rossi G, Giaccone G, Tagliavini F, Nacmias B, Piaceri I, Bagnoli S, Bruni AC, Maletta RG, Bernardi L, Postiglione A, Milan G, Franceschi M, Puca AA, Novelli V, Barlassina C, Glorioso N, Manunta P, Singleton A, Cusi D, Hardy J, Momeni P. A genome-wide screening and SNPs-to-genes approach to identify novel genetic risk factors associated with frontotemporal dementia. Neurobiol Aging 2015; 36:2904.e13-26. [PMID: 26154020 PMCID: PMC4706156 DOI: 10.1016/j.neurobiolaging.2015.06.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2015] [Revised: 05/29/2015] [Accepted: 06/05/2015] [Indexed: 12/14/2022]
Abstract
Frontotemporal dementia (FTD) is the second most prevalent form of early onset dementia after Alzheimer's disease (AD). We performed a case-control association study in an Italian FTD cohort (n = 530) followed by the novel single nucleotide polymorphisms (SNPs)-to-genes approach and functional annotation analysis. We identified 2 novel potential loci for FTD. Suggestive SNPs reached p-values ∼10−7 and odds ratio > 2.5 (2p16.3) and 1.5 (17q25.3). Suggestive alleles at 17q25.3 identified a disease-associated haplotype causing decreased expression of –cis genes such as RFNG and AATK involved in neuronal genesis and differentiation and axon outgrowth, respectively. We replicated this locus through the SNPs-to-genes approach. Our functional annotation analysis indicated significant enrichment for functions of the brain (neuronal genesis, differentiation, and maturation), the synapse (neurotransmission and synapse plasticity), and elements of the immune system, the latter supporting our recent international FTD–genome-wide association study. This is the largest genome-wide study in Italian FTD to date. Although our results are not conclusive, we set the basis for future replication studies and identification of susceptible molecular mechanisms involved in FTD pathogenesis.
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Affiliation(s)
- Raffaele Ferrari
- Department of Molecular Neuroscience, Institute of Neurology, UCL, London, UK; Laboratory of Neurogenetics, Department of Internal Medicine, Texas Tech University Health Science Center, Lubbock, TX, USA.
| | - Mario Grassi
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Erika Salvi
- Department of Health Sciences, University of Milan at San Paolo Hospital, Milan, Italy
| | | | - Fernando Palluzzi
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Daniele Pepe
- Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Francesca D'Avila
- Department of Health Sciences, University of Milan at San Paolo Hospital, Milan, Italy
| | | | | | - Innocenzo Rainero
- Neurology I, Department of Neuroscience, University of Torino and Città della Salute e della Scienza di Torino, Turin, Italy
| | - Elisa Rubino
- Neurology I, Department of Neuroscience, University of Torino and Città della Salute e della Scienza di Torino, Turin, Italy
| | - Lorenzo Pinessi
- Neurology I, Department of Neuroscience, University of Torino and Città della Salute e della Scienza di Torino, Turin, Italy
| | - Luisa Benussi
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giuliano Binetti
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Daniela Galimberti
- Neurology Unit, Department of Pathophysiology and Transplantation, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Policlinico, Milan, Italy
| | - Elio Scarpini
- Neurology Unit, Department of Pathophysiology and Transplantation, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Policlinico, Milan, Italy
| | - Maria Serpente
- Neurology Unit, Department of Pathophysiology and Transplantation, University of Milan, Fondazione Cà Granda, IRCCS Ospedale Policlinico, Milan, Italy
| | - Giacomina Rossi
- Division of Neurology V and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano Italy
| | - Giorgio Giaccone
- Division of Neurology V and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano Italy
| | - Fabrizio Tagliavini
- Division of Neurology V and Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano Italy
| | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Irene Piaceri
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Silvia Bagnoli
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Amalia C Bruni
- Neurogenetic Regional Centre ASPCZ Lamezia Terme, Lamezia TErme, Italy
| | | | - Livia Bernardi
- Neurogenetic Regional Centre ASPCZ Lamezia Terme, Lamezia TErme, Italy
| | - Alfredo Postiglione
- Department of Clinical Medicine and Surgery, University of Naples Federico II, Naples, Italy
| | - Graziella Milan
- Geriatric Center Frullone-ASL Napoli 1 Centro, Naples, Italy
| | | | - Annibale A Puca
- Department of Medicine and Surgery, University of Salerno, Baronissi, Salerno, Italy; Cardiovascular Research Unit, IRCCS Multimedica, Milan, Italy
| | - Valeria Novelli
- Department of Molecular Cardiology, IRCCS Fondazione S. Maugeri, Pavia, Italy
| | - Cristina Barlassina
- Department of Health Sciences, University of Milan at San Paolo Hospital, Milan, Italy
| | - Nicola Glorioso
- Hypertension and Related Disease Centre, AOU-University of Sassari, Sassari, Italy
| | - Paolo Manunta
- Chair of Nephrology, Nephrology and Dialysis and Hypertension Unit, San Raffaele Scientific Institute, Università Vita Salute San Raffaele, Milano, Italy
| | - Andrew Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Daniele Cusi
- Department of Health Sciences, University of Milan at San Paolo Hospital, Milan, Italy; Institute of Biomedical Technologies, Italian National Research Council, Milan, Italy
| | - John Hardy
- Department of Molecular Neuroscience, Institute of Neurology, UCL, London, UK
| | - Parastoo Momeni
- Laboratory of Neurogenetics, Department of Internal Medicine, Texas Tech University Health Science Center, Lubbock, TX, USA
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25
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Proteomic and genomic evidence implicates the postsynaptic density in schizophrenia. Mol Psychiatry 2015; 20:424-32. [PMID: 25048004 DOI: 10.1038/mp.2014.63] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 04/04/2014] [Accepted: 05/19/2014] [Indexed: 12/14/2022]
Abstract
The postsynaptic density (PSD) contains a complex set of proteins of known relevance to neuropsychiatric disorders, and schizophrenia specifically. We enriched for this anatomical structure, in the anterior cingulate cortex, of 20 schizophrenia samples and 20 controls from the Stanley Medical Research Institute, and used unbiased shotgun proteomics incorporating label-free quantitation to identify differentially expressed proteins. Quantitative investigation of the PSD revealed more than 700 protein identifications and 143 differentially expressed proteins. Prominent among these were altered expression of proteins involved in clathrin-mediated endocytosis (CME) (Dynamin-1, adaptor protein 2) and N-methyl-D-aspartate (NMDA)-interacting proteins such as CYFIP2, SYNPO, SHANK3, ESYT and MAPK3 (all P<0.0015). Pathway analysis of the differentially expressed proteins implicated the cellular processes of endocytosis, long-term potentiation and calcium signaling. Both single-gene and gene-set enrichment analyses in genome-wide association data from the largest schizophrenia sample to date of 13,689 cases and 18,226 controls show significant association of HIST1H1E and MAPK3, and enrichment of our PSD proteome. Taken together, our data provide robust evidence implicating PSD-associated proteins and genes in schizophrenia, and suggest that within the PSD, NMDA-interacting and endocytosis-related proteins contribute to disease pathophysiology.
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26
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Van der Sluis S, Dolan CV, Li J, Song Y, Sham P, Posthuma D, Li MX. MGAS: a powerful tool for multivariate gene-based genome-wide association analysis. ACTA ACUST UNITED AC 2014; 31:1007-15. [PMID: 25431328 PMCID: PMC4382905 DOI: 10.1093/bioinformatics/btu783] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Accepted: 11/19/2014] [Indexed: 01/10/2023]
Abstract
Motivation: Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype–phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. Conclusion: MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype–phenotype models. Availability and implementation: MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Contact: mxli@hku.hk Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sophie Van der Sluis
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands
| | - Conor V Dolan
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands
| | - Jiang Li
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands
| | - Youqiang Song
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics
| | - Pak Sham
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands
| | - Miao-Xin Li
- Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics, Section Clinical Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU Medical Center, Amsterdam, The Netherlands, Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands,Department of Biochemistry, State Key Laboratory for Cognitive and Brain Sciences, The Centre for Reproduction, Development and Growth, The Centre for Genomic Sciences and Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong and Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), VU University Amsterdam, Amsterdam, The Netherlands Department of Complex Trait Genetics
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Aslibekyan S, Almeida M, Tintle N. Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18. Genet Epidemiol 2014; 38 Suppl 1:S86-91. [PMID: 25112195 DOI: 10.1002/gepi.21831] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Pathway analysis, broadly defined as a group of methods incorporating a priori biological information from public databases, has emerged as a promising approach for analyzing high-dimensional genomic data. As part of Genetic Analysis Workshop 18, seven research groups applied pathway analysis techniques to whole-genome sequence data from the San Antonio Family Study. Overall, the groups found that the potential of pathway analysis to improve detection of causal variants by lowering the multiple-testing burden and incorporating biologic insight remains largely unrealized. Specifically, there is a lack of best practices at each stage of the pathway approach: annotation, analysis, interpretation, and follow-up. Annotation of genetic variants is inconsistent across databases, incomplete, and biased toward known genes. At the analysis stage insufficient statistical power remains a major challenge. Analyses combining rare and common variants may have an inflated type I error rate and may not improve detection of causal genes. Inclusion of known causal genes may not improve statistical power, although the fraction of explained phenotypic variance may be a more appropriate metric. Interpretation of findings is further complicated by evidence in support of interactions between pathways and by the lack of consensus on how to best incorporate functional information. Finally, all presented approaches warranted follow-up studies, both to reduce the likelihood of false-positive findings and to identify specific causal variants within a given pathway. Despite the initial promise of pathway analysis for modeling biological complexity of disease phenotypes, many methodological challenges currently remain to be addressed.
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Affiliation(s)
- Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
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Greco B, Luedtke A, Hainline A, Alvarez C, Beck A, Tintle NL. Application of family-based tests of association for rare variants to pathways. BMC Proc 2014; 8:S105. [PMID: 25519359 PMCID: PMC4143675 DOI: 10.1186/1753-6561-8-s1-s105] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Pathway analysis approaches for sequence data typically either operate in a single stage (all variants within all genes in the pathway are combined into a single, very large set of variants that can then be analyzed using standard "gene-based" test statistics) or in 2-stages (gene-based p values are computed for all genes in the pathway, and then the gene-based p values are combined into a single pathway p value). To date, little consideration has been given to the performance of gene-based tests (typically designed for a smaller number of single-nucleotide variants [SNVs]) when the number of SNVs in the gene or in the pathway is very large and the genotypes come from sequence data organized in large pedigrees. We consider recently proposed gene-based tests for rare variants from complex pedigrees that test for association between a large set of SNVs and a qualitative phenotype of interest (1-stage analyses) as well as 2-stage approaches. We find that many of these methods show inflated type I errors when the number of SNVs in the gene or the pathway is large (>200 SNVs) and when using standard approaches to estimate the genotype covariance matrix. Alternative methods are needed when testing very large sets of SNVs in 1-stage approaches.
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Affiliation(s)
- Brian Greco
- Department of Mathematics and Statistics, Grinnell College, 1115 8th Ave, Grinnell, IA 50112, USA
| | - Alexander Luedtke
- Division of Biostatistics, UC Berkeley, 367 Evans Hall, Berkeley, CA 94720, USA
| | - Allison Hainline
- Department of Statistics, Baylor University, 1511 S. 5th St, Waco, TX 76798, USA
| | - Carolina Alvarez
- Department of Biostatistics, Florida International University, 11200 SW 8th St., Miami, FL 33199, USA
| | - Andrew Beck
- Department of Mathematics, Loyola University Chicago, 1052 W Loyola Ave, Chicago, IL 60660, USA
| | - Nathan L Tintle
- Department of Mathematics, Statistics and Computer Science, 498 4th Ave. NE, Dordt College, Sioux Center, IA 51250, USA
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Taliun D, Gamper J, Pattaro C. Efficient haplotype block recognition of very long and dense genetic sequences. BMC Bioinformatics 2014; 15:10. [PMID: 24423111 PMCID: PMC3898000 DOI: 10.1186/1471-2105-15-10] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2013] [Accepted: 12/18/2013] [Indexed: 11/10/2022] Open
Abstract
Background The new sequencing technologies enable to scan very long and dense genetic sequences, obtaining datasets of genetic markers that are an order of magnitude larger than previously available. Such genetic sequences are characterized by common alleles interspersed with multiple rarer alleles. This situation has renewed the interest for the identification of haplotypes carrying the rare risk alleles. However, large scale explorations of the linkage-disequilibrium (LD) pattern to identify haplotype blocks are not easy to perform, because traditional algorithms have at least Θ(n2) time and memory complexity. Results We derived three incremental optimizations of the widely used haplotype block recognition algorithm proposed by Gabriel et al. in 2002. Our most efficient solution, called MIG ++, has only Θ(n) memory complexity and, on a genome-wide scale, it omits >80% of the calculations, which makes it an order of magnitude faster than the original algorithm. Differently from the existing software, the MIG ++ analyzes the LD between SNPs at any distance, avoiding restrictions on the maximal block length. The haplotype block partition of the entire HapMap II CEPH dataset was obtained in 457 hours. By replacing the standard likelihood-based D′ variance estimator with an approximated estimator, the runtime was further improved. While producing a coarser partition, the approximate method allowed to obtain the full-genome haplotype block partition of the entire 1000 Genomes Project CEPH dataset in 44 hours, with no restrictions on allele frequency or long-range correlations. These experiments showed that LD-based haplotype blocks can span more than one million base-pairs in both HapMap II and 1000 Genomes datasets. An application to the North American Rheumatoid Arthritis Consortium (NARAC) dataset shows how the MIG ++ can support genome-wide haplotype association studies. Conclusions The MIG ++ enables to perform LD-based haplotype block recognition on genetic sequences of any length and density. In the new generation sequencing era, this can help identify haplotypes that carry rare variants of interest. The low computational requirements open the possibility to include the haplotype block structure into genome-wide association scans, downstream analyses, and visual interfaces for online genome browsers.
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Affiliation(s)
- Daniel Taliun
- Center for Biomedicine, European Academy of Bolzano/Bozen (EURAC), Bozen-Bolzano, Italy.
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Incorporating prior knowledge to increase the power of genome-wide association studies. Methods Mol Biol 2014; 1019:519-41. [PMID: 23756909 DOI: 10.1007/978-1-62703-447-0_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.
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